Data Visualisation

HCI 0283 Lecture 2 Rearrangement Data Visualisation

Rearrangement • Simply rearranging data can lead to an increase in insight into the data • If the rearrangement is done using a computer, so that it is rapid and easy to undo, rearrangement along becomes a very powerful visualisation tool • Rearrangement does not, however, need a computer • One of the most famous rearrangements was done entirely by hand…

The Alchemical Chart of Affinities, 1778 • Alchemy was the origin of modern chemistry • This table charts the ‘affinities’ or interactions between chemical elements such as  (gold)  (silver) and ♂ (iron)

The Telluric Helix, 1832 • The first time that atomic weight was used to classify elements • Oxygen had weight 16; all other elements related to that • The first table to introduce the idea of periodicity

Newland’s Octaves, 1864 • The elements are organised by atomic mass • Each row shows a set of elements, each of which has similar chemical properties

Mendeléev, 1869 • Mendeléev’s big idea was to leave gaps in the table where no element existed • These predicted elements were later discovered

Ordering on electron shell filling • Makes sense if you’re interested in electrons • Non-sequential atomic numbers

‘Standard’ Periodic Table • Ordered by atomic number

Other possibilities • See http://www.meta-synthesis.com/webbook/35_pt/pt.htm

Rearrangement • Suppose we have a sample of ten crops (rice, wheat, beans etc) which have been treated with seven different treatments (fertilisers etc) • Each field has any change noted as white (an improvement) or blue (a degradation) • The crops are numbered 1-10 and the treatments A-G

Rearrangement • In this arrangement it is very difficult to see if there are any relationships in the data • Suppose we cut the diagram into columns and rearrange the order of those columns such that the blue squares are placed as far left as possible, first in row A, then row B etc…

Rearrangement • We can now see some sort of pattern emerging • Suppose we also cut the diagram into rows and rearrange the order of those • We still have the same data but the presentation has changed

Rearrangement • It is clear now that there are certain groups of treatments that are useful to certain crops • This information might influence future planting or treatment patterns • This is just one reconstruction; reconstructing things in different ways leads to different insights into the data

The Table Lens • Repeated rearrangement is an interactive process that is well suited to using a computer • The Table Lens, developed by Rao and Card in 1994, allows data to be displayed in many different histograms • The Table Lens for US Crop Data • A big advantage of the table lens is that it is based on histograms or tables, which are probably the most familiar of all data representations • The Table Lens is not only a good representational tool, it is also easy to use, which encourages people to spend time exploring the data

Coffee Time!

The Mosaic Display • The previous two rearrangements were very simple • A further rearrangement technique is to use a Mosaic Display • Once upon a time there was a lecturer who recorded the eye and hair colours for each of his 592 students…

Mosaic Display • It is not immediately clear if there are any relationships within the data • Instead, we create a diagram of that each rectangle has a height proportional to the number of students with a particular eye colour and a width proportional to the number of students with a particular hair colour

Mosaic Displays • If eye colour is independent of hair colour then the area of each rectangle would be proportional to the probability of the occurrence of any combination of hair and eye colour • We will change the diagram so the width is still the relative occurrence of hair colour but the height is now governed by the actual number of students with an eye colour

Mosaic Displays • If hair colour and eye colour were independent then the rows would be equal in height • In fact there are more blue-eyed blonds and brown-eyed, black-haired people • We can rearrange this to show the deviation between hair colour and eye colour more easily

Mosaic Displays • This diagram allows us to see relationships within hair colour groups as these are aligned • If we want to show variations within eye colour we would create a new diagram with eye colours determining the width and hair colours determining the height • We’ll look at some extensions of this diagram in the next lecture

Network Data • We can also rearrange network or connectivity data • Suppose we record phone calls made between 13 people (labelled A to M) • It is very difficult to identify any interesting patterns from the data in either table or node-link diagram forms

A B M C L D K E J F I G H

Network Data • If we process the data a little to identify unconnected subgraphs and represent these by node-link diagrams we see more interesting features • It is clear that there are three distinct groups of users • If we had further information such as duration we could encode the information to gain further insight

H A B F K E G I J C M L D

B K E G I M

Summary • Rearrangement is a simple but powerful tool for making less obvious features of data clearer • Interactive rearrangement of data provides additional insight into data • It can be used for all data types • Categorical (crops/treatments, eye/hair colours) • Numerical (agricultural statistics) • Topological (telephone connections)

Coming Soon… • Next lecture: Quantitative Data • Homework: Read chapter 2 of Information Visualisation (Spence)

Data Visualisation